Publication: Algorithmic Differentiation Applied to Economics
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Algorithmic Differentiation Applied to Economics

- Part of a collection -
 

Area
Economics

Author(s)
E. M. Tadjouddine

Published in
Proceedings of the of the International MultiConference of Engineers and Computer Scientists 2009 (IMECS 2009), Hong Kong, March 18--20, 2009

Editor(s)
S. I. Ao, O. Castillo, C. Douglas, D. D. Feng, J. -A. Lee

Year
2009

Publisher
Newswood Limited

Abstract
This paper discusses the use of the Automatic Differentiation approach in evaluating derivatives of functions represented by computer programs. We then considered a Cournot oligopoly modeled by a system of stochastic differential equations. The setting is that of a set of self-interested firms striving to adjust their productions in the direction of higher profits subject to mistakes or random shocks. The stochastic differential equations are solved by a numerical method and the profits are calculated using a Monte Carlo simulation. Then, Automatic Differentiation is used to propagate sensitivities along each path in an automated fashion. Numerical results have confirmed the intuition one may have that noisy environments can lead to important profit differences between firms as well as higher sensitivities as opposed to less noisy ones.

AD Tools
MAD, TOMLAB /MAD

BibTeX
@INPROCEEDINGS{
         Tadjouddine2009ADA,
       author = "E. M. Tadjouddine",
       title = "Algorithmic Differentiation Applied to Economics",
       booktitle = "Proceedings of the of the International MultiConference of Engineers and Computer
         Scientists 2009 (IMECS 2009), Hong Kong, March 18--20, 2009",
       year = "2009",
       editor = "S. I. Ao and O. Castillo and C. Douglas and D. D. Feng and J.-A. Lee",
       volume = "2",
       pages = "2199--2204",
       organization = "International Association of Engineers",
       publisher = "Newswood Limited",
       abstract = "This paper discusses the use of the Automatic Differentiation approach in
         evaluating derivatives of functions represented by computer programs. We then considered a Cournot
         oligopoly modeled by a system of stochastic differential equations. The setting is that of a set of
         self-interested firms striving to adjust their productions in the direction of higher profits
         subject to mistakes or random shocks. The stochastic differential equations are solved by a
         numerical method and the profits are calculated using a Monte Carlo simulation. Then, Automatic
         Differentiation is used to propagate sensitivities along each path in an automated fashion.
         Numerical results have confirmed the intuition one may have that noisy environments can lead to
         important profit differences between firms as well as higher sensitivities as opposed to less noisy
         ones.",
       ad_area = "Economics",
       ad_tools = "MAD, TOMLAB /MAD"
}


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